Random numbers are typically necessary in cryptography, statistical research, including quantum cryptography, statistical research (Monte Carlo simulations in physics, biology, economics, etc.), randomized algorithms, etc. True random numbers (e.g., random numbers generated based on physical processes rather than by software tools) are typically required in our everyday life: mobile communications, e-mail access, online payments, cashless payments, ATMs, e-banking, Internet trade, point of sale, prepaid cards, wireless keys, general cybersecurity, distributed power grid security, etc.
True random number generators (TRNG) typically work by providing a source of truly random numbers that do not come from a mathematical process, such as those used to generate pseudo random numbers in pseudo random number generators (PRNG). Source of true randomness can be from, for example, radioactive decay (typically slow), the chaotic motion of fluids (typically very slow), atmospheric noise (typically slow), quantum-based, or from other unpredictable systems that cannot be guessed by another even with access to a similar or the same device.
The generation of true random numbers at rates higher than 1 Gbit/s is an unmet market need, and according to certain recent publications, the market is in need of stable high throughput TRNGs with throughputs of up to 30 Gbit/sec. Indeed, currently available high-throughput TRNGs are costly and their number generation rates are limited to sub-Gbit/s rates. Many of these currently available TRNGs employ various sources of entropy (e.g., shot noise, thermal noise, and reverse biased Zener diodes), which are typically cheaper, less reliable, and provide lower throughput, while quantum optical TRNGS are able to provide very high more robust throughput.